Consistent and accurate methods for performing state estimation in a wide-variety of systems are critical to the function of many processes and operations, both civilian and military. Systems and methods have been developed for state estimation of a system that may transition between different regimes of operation which may be described or defined by a plurality of discrete models. These state estimation methods can be applied to various systems having sensory inputs, by way of non-limiting example only, nuclear, chemical, or manufacturing factories or facilities, control processes subject to external parameter changes, space stations subject to vibrations, automobiles subject to road conditions, and the like. One particularly useful application for state estimation is tracking objects in flight, such as a multistage rocket that is transitioning back and forth between a ballistic model of flight and other thrust models, or an aircraft performing maneuvers mid-flight.
As will be set forth in greater detail below, current state estimation systems and their associated algorithms have difficulty estimating the state of a system transitioning between distinct regimes of operation (i.e. between different, non-interacting models). More recent algorithms may implement “interacting models”, including Interacting Multiple Models (IMM), which allow for transitioning from one model to another during the estimation process. These interacting model algorithms have the advantage of reducing the filter lag and/or noise when the system transitions from one model to another. However, they are often burdened by computational challenges. Likewise, existing IMM-based estimators and their associated filtering arrangements also suffer from significant drawbacks, as their designs typically require large amounts of simulation, making their implementation impractical.
Improved systems and methods for state estimation are desired.